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ContentslistsavailableatScienceDirect

Energy

and

Buildings

jo u r n al h om ep a g e :w w w . e l s e v i e r . c o m / l o c a t e / e n b u i l d

Spatial

planning

as

a

driver

of

change

in

mobility

and

residential

energy

consumption

S.

Dujardin

a

,

A.-F.

Marique

b

,

J.

Teller

b,∗

aLepurUniversitédeLiège,1ChemindesChevreuils,B52,4000Liège1,Belgium bLEMAUniversitédeLiège,1ChemindesChevreuils,B52,4000Liège1,Belgium

a

r

t

i

c

l

e

i

n

f

o

Keywords: Spatialplanning Energyconsumption Buildingstock Mobility

a

b

s

t

r

a

c

t

ThispaperanalysestheimpactofterritorialstructuresuponenergyconsumptionintheWalloonRegion (Belgium).Therationaleforthisresearchistoconsiderthelong-terminfluenceofspatialplanning deci-sionsuponenergyconsumptioninbothresidentialbuildingstockandhome-to-workcommuting.The analysishasbeenconductedonaregionalscale(16,844km2)andincludesurban,peri-urbanandrural

settlements.Thosesettlementsthatperformwellinmobilityalsoappeartoperformwellintermsof buildingenergyconsumption.Eventhoughthisisnotgenerallythecase,itfurtherrevealsthatsome ruralsettlementscharacterizedbylowdensityshowgoodperformanceintermsofenergyefficiency. Thispermitsamuchmoreprogressiveapproachintermsofspatialplanning,wherebycompactcities maybeviewedaspartofthesolution,albeitnotthewholesolution.

©2013ElsevierB.V.Allrightsreserved.

1. Introduction

Theinfluenceofthespatialpatternofhumanactivitiesonenergy consumptioninthetransportationand/orthebuildingsectorshas beenthesubjectofagreatdealofempirical,theoreticalandpolicy research.

Basedontheobservationthatexistingcomputermodelsadopt theperspectiveoftheindividualbuildingasanautonomousentity andneglectphenomenalinkedtolargerscales[1],agrowingbody ofliteraturesincethelate1990shasexploredtheeffectsofurban structuresonbuildingenergyconsumption.Ithighlightsthat deci-sionsmadeattheneighbourhoodandcitylevelsregardingbuilt volumeandsurface,orientationoffac¸adesandobstructionshave importantconsequencesfortheperformanceofindividual build-ingsinheating,ventilationandcooling[1–3].Conversely,forthe same level of insulation, lower density and detached types of housestendtorequiremoreenergytoheatthanmulti-unit devel-opmentsorterracedhousing[4,5].Inthesamevein,theEnergy andEnvironmentPrediction(EEP)model[6]isbasedonaregional databasethatprovidesenergyconsumptionfiguresfor100 build-ingtypes.Thevariablesconsideredinthetypologyareheatedfloor area,facadearea,windowpercentageandage.Integratingthese valuesintoaGeographicInformationSystem(GIS)allows compar-isonofenergypoliciesatthecitylevel.Ithighlightsthemagnitude

∗ Correspondingauthor.Tel.:+3243669499;fax:+3243669531. E-mailaddress:Jacques.Teller@ulg.ac.be(J.Teller).

ofpotentialenergysavingsattheurbanlevelthrougharenewalof existingbuildingstock.

The relationship between urban form and transport energy consumptionisalsodiscussed.Basedondatafrom32large inter-nationalcities,NewmanandKenworthy[7,8]highlightedastrong inverserelationshipbetweenurbandensityandtransportenergy consumption.Nonetheless,theirworkisonlyvalidundercertain conditionsandisoftencriticizedbyotherscholars[9,10]mainlyfor methodologicalreasons.Bannister[11]appliedasimilarapproach to British cities, but based on statistical data obtained from a nationalsurvey.Hedemonstratedthattransportationenergy con-sumptionisslightlyhigherinLondonthaninsmallercities,which refutesNewmanandKenworthy’sobservations.BoarnetandCrane [12]arealsoscepticalabouttherelationshipbetweenurbandesign and transportation behaviour. By analysing case studies, they suggestedthattheuseoflandandtheurbanformimpact trans-portationbehaviourbecauseofthepriceoftravel(publictransport prices arereducedindenseareas).Gordonand Richardson[13] demonstratedthaturbandensityonlyplaysalimitedroleinenergy consumptionintransportiffuelpricesareincludedintheanalysis. InthesampleofcitiesusedbyNewmanandKenworthy,Breheny and Gordon [14] demonstratedthat thedensity coefficient and itsstatisticalsignificancedecreasewhenpetrolpriceandincome areincludedasexplanatoryvariables.Breheny[15]emphasized minorreductionsintransportationenergyconsumptionbecause ofthecompactcitymodel.Hisexperimentsshowedthatenergy usedintransportcouldonlybereducedby10–15%,evenunder verystrictconditionsthataredifficulttoreproduce.Bystudying 10 cities around theworld, Souche[16] showed that themost

0378-7788/$–seefrontmatter©2013ElsevierB.V.Allrightsreserved.

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780 S.Dujardinetal./EnergyandBuildings68(2014)779–785

statisticallysignificantvariablesfortransportenergyconsumption aretransportcostandurbandensity.Finally,EwingandCervero [17] highlightedthat percapita vehicle travel tendsto decline andtheuseofalternativemodestoincreasewitharisein den-sity.Fortheseauthors,compactdevelopments,whichreflectthe cumulativeeffectsofincreaseddensity,functionalmixand tran-sitaccessibility,typicallyreducethepercapitavehicletravelby 25–30%.Similarly,Stead[18]foundthatif43%ofthevariationin distancestravelledisexplainedbysocio-economicvariables,27% ofthisvariationisdirectlyrelatedtoland-usevariables,whichis considerable.

Variousstudiesarguethatmorecompacturbanformswould significantlyreduceenergyconsumption,inboththebuildingand transportationsectors[2,19–21]bycombiningsuchfactorsashigh density,mixinglandusesandabettershareofactivecommuting, whereasotherauthors[22]indicatethatlowerenergy consump-tionmaybeachievedbydecentralizedconcentration.

Considering this background, the present paper specifically analysestheimpactofterritorialstructuresonenergy consump-tionintheWalloonRegion(Belgium),examiningbothresidential buildingstockand home-to-work commuting. Territorial struc-turesarediscussedhereintermsofthreemaincomponents:the locationofhouseholds,thelocationofemploymentandmobility infrastructure(roads,buses,trains).Itisconsideredthatthe inter-actionbetweenthesethreecomponentsisastructuralproperty ofaterritorythatmayaffectenergyuseviamobilityandhousing consumptionpatterns.Increasing householddensities generally entailmorecompactbuildings(terracedhousesandapartments), which tendsto lessen energy losses. Mixing placesof employ-mentandhouseholdsallowspeopletofindjobsatcloserlocations, whichmayreducethedistancestheytraveltowork,orto destina-tionsforpurposessuchasshoppingorleisure.Adequateaccessto transportationfacilitiesmayimpacttravelmodes,andindirectly, housingdensitiesandenergyconsumption.Obviouslyitshouldbe acknowledgedthatthereisanimportantbehaviouraldimension intheserelations[23].Theproximityofjobsdoesnotconstitute aguarantee that householders willeffectivelyselecta job near home.Developingtheanalysisonastatisticalbasisreveals empir-icaltrendsintherelationbetweenthesevariablesandobserved behaviour.

Thecombinationofthesethreevariablesisassumedtobean elementthatcansomehowbehandledby urbanplanning poli-cies.Theeffectiveinfluenceofurbanplanninguponemployment andhouseholdlocationsisobviouslylimited[15].Stillitshould beacknowledgedthatplanningpoliciesataEuropeanlevellead tostrikingdifferencesinthisrespect,asisevidentintherelative extentofsprawlindifferentregions[24].

Accordingly, Section 2 describes the general methodology adoptedinthisresearch,whichisbasedonacombinationof Geo-graphicInformationSystemswithsurveyandcadastredataatthe regionallevel. Sections3 and 4 introduce and discuss maps of energyconsumption,respectively,forhome-to-workcommuting

andforresidentialbuildingheatingintheWalloonRegion.Section5 combinesobservedresultsandindicatorsofdensityandmixeduse tohighlighttheimpactofterritorialstructuresonenergy consump-tion.Theconcludingsectionisageneraldiscussionoftheresults andpossiblepolicyrecommendationsregardingspatialplanning policies.

2. Methodology

Theoverallmethodologyofthisresearchisbasedonspatial cor-relationsbetweenenergy performanceindicators,namelymean home-to-workcommuteenergyconsumptionandmean residen-tialbuildingenergyconsumption,withtwoterritorialindicators, namelymeandensityandmixeduse.Eachofthesefourindicators hasbeencalculatedonthescaleofstatisticalunits.Theterritoryof theWalloonRegioniscoveredby9876statisticalunits.Thearea ofthesestatisticalunitsvariesbetween1.3haand5834hawitha medianvalueof47.7ha,whichcorrespondstoacircleofslightly less than400min radius.Statistical unitscorrespondto neigh-bourhoodsinurbanareasandencompasslargedepopulatedzones inruralareas.It isimportanttonotethattheanalysishasbeen conductedforallstatisticalunitsintheentireregion(16,844km2) andincludesurban,peri-urbanandruralsettlements.Thisisan importantdifferencefromtheapproachdevelopedbyNewmanand Kenworthy[7],whodeliberatelyfocusedonlargescale agglomer-ations.

Forhome-to-workcommute,themodelisbasedonthegeneral surveyundertakeninBelgiumevery10yearsamongstallcitizens over16yearsofage.Thesurveyprovidesfiguresabout home-to-workdistancestravelledbyworkersandtheirchoiceofmodeof travel.Altogether,datafrom8,572,000respondentswereextracted fromthecensussurvey.Thisrepresentsapproximately73.1% of Wallonia’sworkingpopulationin2001.Thesedatawereusedto builda mobilityenergyperformanceindex,followingBoussauw andWitlox[25].Itwascalculatedfor1991and2001,corresponding tothetwomostrecentgeneralsurveysinBelgium.Thefollowing conversiontablewasusedtoestimatekWhandCO2emissionsper kilometretravelledandpassengeronthevariousmodesoftravel. Table1alsoprovidesregionalenergyconsumptionandCO2 emis-sionsforhome-to-workcommuting,consideringannualdistance travelledandmodechoiceofallrespondentstothesurvey.

FiguresforenergyconsumptionandCO2emissionsper kilome-tretravelledand passenger wereobtainedbydividingthetotal amountofenergyconsumedforagiventravelmode,calculated onthebasisoftheannualkilometrestravelledandfueltype,by theoccupancyrateofthatmode.Conversionfactorsforelectricity toCO2 emissionswereprovidedbytheWalloonAirandClimate Agency(AWAC).Detailsofthecalculationwerepublishedin[26]. Ameannumberofpassengersforeachtravelmodeisconsidered here;thecalculationdoesnotconsiderknownvariationsofenergy consumptionwithinpublictransportaccordingtooccupancyrate.

Table1

SpecificenergyconsumptionandCO2emissionsbytravelmodeintheWalloonRegion.

Mode Modal

share2001

(%)

Energyconsumptionbykm

travelledandpassenger

(kWhperkm)

CO2emissionsbykmtravelled

andpassenger(gCDEperkm

[gramofCO2equivalentper

passengerandkm])

Regionalenergy

consumptionsfor

home-to-workcommute

(GWh)

RegionalCO2emissionsfor

home-to-workcommute

(TCDE[metrictonneofCO2 equivalent])

Car 80.2 0.45 118.3 18,722.0 4894.7

Moto,scooter 1.9 0.41 105.0 251.4 64.6

Bus,tram,metro 4.1 0.35 79.5 417.6 93.7

Train 7.2 0.15 35.7 451.1 104.4

Bike 1.2 – – – –

Walking 5.4 – – – –

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Table2

ThermalperformanceofenvelopesbybuildingperiodintheWalloonRegion.

Buildingperiod Shareofthehousing

stock(%)

Uwall(W/m2K) Uwindow(W/m2K) Uroof(W/m2K) Ufloor(W/m2K) Ventilation

rate(V/h) Window(%) Before1945 52 2.2 3.3 1.6 1.9 1 24 1945–1970 19 1.4 3.3 1.4 1.5 1 27 1971–1985 15 0.8 3 1.0 2.4 0.9 25 1986–1996 6 0.5 2.6 0.9 0.7 0.9 25 1997–2010 8 0.5 2.4 0.7 0.7 0.7 26

Itishenceunfavourabletourbantransport,whichhashigher occu-pancyrates thanin ruralareas.Forinstance, theratiobetween specificemissionsofacarwhencomparedwiththatofatrainis about3:1.Therelativelysmalldifferencebetweenthetwomodes canbeexplainedbythefactthatthefiguresarebasedonregional figuresandmeanoccupationrates.Itcanfurtherbeobservedin Table1thatmorethan80%ofhome-to-worktravelisbycarinthe WalloonRegion.Theaveragetriplengthwas23.3kmin2001and thisgrewby16%between1991and2001.

Regardingresidentialbuildingenergyconsumption,themodel wasbasedoncombiningcadastresurveyswithbuildingheights providedbyphotogrammetricdataforsome850,000residential buildingsintheWalloonRegion,whichcorrespondstosome66% ofresidentialbuildingstock.Thecadastreprovidestheageof build-ings. This variable was usedto estimate envelope and heating systemperformanceforeachbuilding,followingtheapproachused byMaïziaetal.[27]andJonesetal.[6].

Energyconsumptionofbuildingswasthenestimatedusinga conventionaldegree-daysmethod[28].Thismethodconsidersthe insulationoftheenvelope,internalandsolargainsaswellasthe degree-daysregisteredclosetothelocationofthebuilding.Amean valuewasusedfortheorientationofbuildings.Itwasconsidered that there isno preferredmean orientationof buildings in the existingurbanfabric[27].Solarmasksvarybetween0%and40% accordingtothedensityoftheurbanfabricwherethebuildingis located.Theageofbuildingswasusedtoestimatetheperformance oftheheatingsystem.

Building periods were defined to match important techni-cal/thermalturningpoints,forinstancetheadoption/changesof thermalregulationsin the WalloonRegion (in 1985,1996 and 2010). Table 2 provides an overview of thermal performance parameters used in themodel. It is based onthe most recent housingsurvey undertaken in the WalloonRegion, which pro-videsinformationabout insulationof buildings for asample of 6000dwellings[29].InformationfromCarlieretal.[29]wasused todetermine,foreachbuildingperiod,theproportionofexisting buildingsthatisisolated,consideringthefourmaincomponentsof theenvelopethatwereconsideredinthesurvey:slabs,wallsand roofs,aswellastypeofglazinginthebuilding.Thisinformation foreachperiodwascombinedwithconstructiontypestoderive typicalenvelopecompositions,withandwithoutinsulation.

ItcanbeseeninTable2thatthethermalperformanceof res-identialbuildingenvelopesisverypoorforolderbuildingsinthe WalloonRegion.Policymeasuresestablishedintheearly1980sto stimulatetherefurbishmentofbuildings,whethertaxincentivesor directsubsidiesforhomeowners,hadaverylimitedimpactonthe improvementoftheenergyperformanceofthebuildingstock.They mostlyconcernedwindowsand windowframes;a large major-ityofexternalwallsandevenroofshavenotyetbeeninsulated, whileinsulatingtheroofisoftenpresentedasthemosteffective anddurablemeasuretoimprovetheenergyperformanceofthe buildingstock[30].

Itshouldbestressedinthisrespectthatin2010,some52%of thehousingstockintheWalloonRegionpredated1945;87%of thestockwasbuiltbefore1985whenthefirstregulationsonthe insulationofnewbuildingswereadopted.

3. Home-to-workcommuteenergyconsumption

Theanalysishighlightsanincreaseof20%inenergy consump-tion per kilometre travelledby each passenger in theWalloon Regionbetween1991and2001.Thisismainlyduetotheextent ofsprawlandamodificationoftheemploymentcatchmentareas ofBrusselsandLuxembourgCity.Itisstrikingthatamongstthe20 municipalitiesthatwitnessedthelargestincreasesintheir emis-sionsrelatedtomobility,18arelocatedinthesouthoftheregion andpolarizedbyLuxembourgCity.

Fig. 1 highlights that urbanareas locatedalongthe old and dense“Liege–Charleroi–Mons”industrialaxisarecharacterizedby lowerenergyconsumption.Attheoppositeextreme,ruraland peri-urbanareasarecharacterizedbymuchhigherenergyconsumption. Thesituationofperi-urbanareasisespeciallychallengingbecause theynowattractlargepopulations,especiallyinthesouthof Brus-sels.Interestingly,someoftheseperi-urbanareasexperienceda decreaseorstabilizationintheirenergyconsumptionfor home-to-workcommuteoverthe1991–2001period,duetotherelocation ofjobsoutsidemainagglomerations.

Intermsofurbanplanning,thisshouldleadtocontrasting solu-tionsinthevariousurbanpatterns.Althoughurbanareasshow betterperformancethanruralandperi-urbanareas,energy con-sumption perkilometre travelledand passenger shouldstill be reduced becausetheygather a largeproportionof theregional populationandnumberoftrips.Conversely,populationgrowthin remoteruralareasshouldbecontainedbecauseitusuallyleadsto longdistancesusuallydonebycar.Finally,inperi-urbanareas,a combinationofreconcentrationofhousingandeconomicactivities aroundefficientpublictransporthubsisprobablythebestoption tocurbthecurrentmobilityandenergyconsumptiontrends. 4. Residentialbuildingenergyconsumption

Whentotalhousingstockisconsidered,theannualenergy con-sumptionbysquaremetreoffloorspaceappearslargelyrelated totheageofconstruction(Table3),althoughitisalsoinfluenced byurbancompactnessandclimatefactors.Itshouldbenotedin thisrespectthatthestandarddeviationisparticularlyimportant forbuildingsbuiltbefore1945.Althoughtheirthermalinsulation isverypoor,thiscanbecompensatedbygreatercompactnessof buildingslocatedindenseurbancentres.

Globally, thethermalperformance ofresidentialbuildings is very poor,with a mean annual consumption of approximately 350kWh/floor m2. It is striking that consolidated urban areas

Table3

Energyconsumptioninbuildingstockbyclassofconstruction.

Meanannualenergy

consumption(kWh/m2) Standarddeviation (kWh/m2) <1945 407.8 163.4 1945–1970 343.7 81.9 1971–1985 328.5 90.7 1986–1996 203.8 35.8 >1996 172.3 40.2

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782 S.Dujardinetal./EnergyandBuildings68(2014)779–785

Fig.1.Energyconsumptionperkmtravelledandpassengerforhome-to-workcommuteintheWalloonRegionatthestatisticalunitlevel.

generallyshowbetterperformancethanperi-urbanareas(Fig.2), especiallyforthosebuildingserectedbefore1985,atatimewhen therewasnoregulationofthermalperformanceofbuildings.The decadesbetween1945and1985correspondtoaperiodofvery intensesprawlintheWalloonRegion.Itshouldbestressedthat detached buildings largely dominated post-war construction in theregion,as intherestofBelgium. Whenthesebuildingsare notinsulated,aswasusuallythecasebefore1985,theirenergy consumptioncanbeveryhigh.

Furthermore,itcanbeobservedthatinsomeremoteruralareas, especiallyinthesouth-eastoftheregion,theperformanceof build-ingsisrathergood,becausesomeofthesesectorshaveexperienced stronggrowthoftheirbuildingstockoverthepast10years.

In dense urban areas, where the network of streets and buildings is consolidated, improvingthe performance of build-ingsshouldnowbe anobjective fullyintegrated intoallurban regenerationpolicies.Bycontrast,inperi-urbanareas,improving buildingperformance could rather be achieved through demo-lition/reconstruction and densification operations, especially in caseswhenthereisaccesstopublictransportandotherservices. 5. Residentialvs.mobilityenergyconsumption

Itisnotpossibletocomparedirectlyhome-to-work consump-tionwithbuildingenergyconsumptionbecausetheseestimations arebasedondistinctassumptions.Ontheonehand,home-to-work consumptionisbasedonreporteddistancesandmodesoftravel whilethecalculationofbuildingenergyconsumption convention-allyassumesthatbuildingsareoccupiedandheatedthroughoutthe day,whichisobviouslynotalwaysthecase.Furthermore, home-to-worktravelrepresentsonlyashareofthetotalkilometrestravelled

in1daybymembersofhouseholds. IntheWalloonRegion,the home-to-workcommuterepresents38%ofthekilometrestravelled onaworkingday,whichaccountsfor22.6%ofthenumberoftrips [31].

Ifitmakeslittlesensetocompareabsolutevaluesofenergy consumptioncalculatedforthesetwodomains,theymaystillbe relatedtoterritorialstructures.Asdocumentedintheliterature,it appearsthattheenergyconsumptionofhome-to-worktraveland theheatingofbuildingsarebothcorrelatedwithhumandensity andmixed use.Nethumandensityisdefined asthenumber of inhabitantsplusjobsdividedbytheurbanizedsurfaceofagiven area[32].Urbanizedareaisdefinedasthesumofparcelsofland occupiedbybuildings.Themixeduseindicatorhasbeendefinedas thenumberofusesoflandthatcanbeobservedwithinaradiusof 500m.Itwascalculatedonagridof10by10mcoveringtheentire landoccupationsurveymapoftheWalloonRegion.

A mean value of all these four indices—mobility energy consumption, buildingenergy consumption, density and mixed use—hasbeencalculatedforallstatisticalsectorsoftheWalloon Region.It wasthen possibletoanalyzethecorrelationbetween thematthisscaleofanalysis.

ItcanbeseenfromTable4thatthecorrelationbetweenenergy consumptionand thetwo urban indicators(density and mixed use)isstatisticallysignificant,althoughthecorrelationcoefficients areratherweak.Thisbasicallymeansthatthosestatisticalsectors withahighermeandensityand/ormixedusevaluearegenerally characterizedbylowerenergyconsumption,bothformobilityand residentialbuildingheating,althoughtherearequiteanumberof sectorswherethisrelationisnotobserved.Itfurtherappearsthat thecorrelationcoefficientofmobilityperformancewithmixeduse ishigherthanthatwithdensity,althoughitshouldbenotedthat

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Fig.2.EnergyconsumptioninresidentialbuildingsintheWalloonRegionatthestatisticalunitlevel.

theselasttwovariables arethemselvescorrelated. Becauseitis directlyrelatedtotheaccessibilityofjobsandserviceswithina shortdistance,mixeduseinfluencespatternsofdisplacementof householdsandindividuals,bothintermsofdistancetravelledand travelmode,becausemixed-useareasareusuallycharacterizedby betteraccesstopublictransportation.

Bycontrast,thecorrelationcoefficientofresidentialbuilding performanceswithdensityishigherthanthatwithmixeduse.This isobviouslyrelated tothefactthat densityisdirectlylinkedto buildingcompactness,whichinfluencesenergyconsumptionviaa reductionofthethermalenveloperelativetosquaremetresoffloor space.Thefactthatbuildingenergyconsumptionisalsorelatedto mixeduseismainlyduetothesubjacentrelationbetweenmixed useanddensity.

TheseresultsraisequestionsregardingassertionsbyNewman and Kenworthy linking energy performance of cities solely to density.First,becausethereissubstantialvariationamongst sta-tisticalunitsalongtheobservedtrend;lowdensityunitsmaybe

Table4

Pearson’scoefficientsofcorrelationbetweenenergyperformanceindicesand indi-catorsofdensityandmixeduse.

Nethumandensity

(inhabitants+jobs/ha)

Netfunctionalmixed

use(nboffunctions) Mobilityenergy consumption (kWh/trippassenger) −0.483** −0.504** Buildingenergy consumption (kWh/floorm2) −0.603** −0.545**

**Correlationsignificantat0.01(2-tailed).

characterizedbyverygoodperformanceespeciallywhentheyare locatedclosetoemploymentcentres(forcommuting)orhavebeen builtrecently(forenergyconsumptioninbuildings).Additionally, theresultsstresstheimportanceofmixedusebesidesdensityfor understandingandinfluencingtravelbehaviour.

Finally,theperformanceofstatisticalunitsalongthetwo dimen-sionsobserveduntilnowcanbecomparedfortheentireregion (Fig.3).Aftergroupingstatisticalunitsbyformermunicipality(the geographicalscaleabovethestatisticalunit),theywereclassified accordingtotheirpositioninahierarchyofeightclassesof munic-ipalitiesinBelgium[33].Thishierarchyisbasedonaclassification ofmunicipalitiesaccordingtotheirsizeandleveloffacilities(e.g., presenceofhighereducationinstitutions,metropolitanservicesor schools).Itdistinguishesfourtypesofmunicipalities:maincities, regionalcities,smalltownsandrural(non-urban)municipalities. Forthelasttwocategories,smalltownsandruralmunicipalities, theclassificationdistinguisheshigh-,medium-andlow-level facil-ities.

Ingeneralterms,itappearsfromFig.3thatthosesectorsthat perform wellinterms ofmobility alsotend toperformwellin termsofbuildingenergyconsumption,andthatthereverseisalso true.Additionally,mostruralsettlementsarelocatedontheupper rightsideofthegraph;theyhavehigherthanaverage consump-tionforbothmobilityandbuilding.Finally,andmostimportantly, allsettlementsarerepresentedonthelowerleftsideofthegraph, whichcorrespondstoenergyconsumptionbelowtheregional aver-ageforbothmobilityandresidentialbuildingheating.Thismeans that good performance isobserved inall eighttypes ofhuman settlements,betheyurban,ruralorperi-urban,dependingonthe distancetocentresofemploymentandspecificitiesoftheurban pattern.

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784 S.Dujardinetal./EnergyandBuildings68(2014)779–785

Fig.3. Scatterdiagramofmobilityandbuildingperformanceindicatorsaccordingtothehierarchyofmunicipalitiesestablishedby[33]forBelgium.

Thisopensanavenueforamuchmoreprogressiveapproachin termsofspatialplanning,wherebycompactcitiesmaybeviewed aspartofthesolution,albeitnotthewholesolution.Indeed, plan-ningpoliciesshouldbetailoredtoeachsettlementtypetomovethe entirescatterofpointsinFig.3tothelowerleftsideofthegraph ratherthanpursueaveryhypotheticalandunrealisticaimto con-centratethepopulationincompactcities.Thiswayofplanning, basedonsharedeffortsamongstallsettlementtypes,isespecially pertinentwhenoneconsiderstheentireterritorialstructureofa regionanditsstronginertiaovertime.

6. Conclusions

Amethodforacombinedanalysisofbuildingandmobility per-formancehasbeenestablishedandappliedtostatisticalunitsin theWalloonRegionof Belgium.Results indicatethat such per-formanceis closely related tothenatureof thestatistical unit, bothin terms of density andmixed use, and that performance alongthesetwo axesisclosely related.It confirmsthatdensity andmixeduseplayanimportantroleinreducingconsumptionfor bothhome-to-worktravelandtheheatingofbuildings. Accord-ingly,spatialplanningpoliciesshouldensurethatthelocationof newbuildingsandeconomicactivitiesisdirectedtowardsexisting settlementswithaviewtodensifyinganddiversifyingtheirbuilt environment.

Theanalysisfurtherhighlightsthatentitieswith better-than-averageperformancecanbeidentifiedinalltypesofsettlements, betheyurban,peri-urbanorrural.Thisisanargumentfor sub-tlersolutionsthanthetraditionalcompactcitymodel,whichisnot

readilyapplicabletoexistingperi-urbanandruralsettlements.In theseconfigurations,priorityshouldbegiventotherelocationof activitiestolessentheirdependencyonremotecentresof employ-ment.Thedevelopmentofthoseareaswherethisisnotpossible shouldprobablybelimitedbyadequatecontainmentpolicies.

Finally,theanalysisrevealstheimportantpotentialforenergy savingsincurrentbuildingstock.Spatialplanningmaybedirected towardsaccelerating the renovationand/or thedensification of existingurbanfabricthroughcontainmentpolicies.Insomecases, itmaybedirectedtoincreasingthesubstitutionofexistinghouses by new ones with contemporary energy standards and higher densities.Thisis especially importantinthose peri-urbanareas characterizedbypoorperformanceoftheirbuildingstockandgood accessibilitybypublictransporttourbancentres.

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Figure

Table 1 also provides regional energy consumption and CO 2 emis- emis-sions for home-to-work commuting, considering annual distance travelled and mode choice of all respondents to the survey.
Fig. 1 highlights that urban areas located along the old and dense “Liege–Charleroi–Mons” industrial axis are characterized by lower energy consumption
Fig. 1. Energy consumption per km travelled and passenger for home-to-work commute in the Walloon Region at the statistical unit level.
Fig. 2. Energy consumption in residential buildings in the Walloon Region at the statistical unit level.
+2

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